Examples with ParameterSpace org.dkpro.lab.task.ParameterSpace used on opensource projects

Example 66 with ParameterSpace

use of org.dkpro.lab.task.ParameterSpace in project dkpro-lab by dkpro.

the class BatchTaskEngine method run.

@Override
public String run(Task aConfiguration) throws ExecutionException, LifeCycleException {
    if (!(aConfiguration instanceof BatchTask)) {
        throw new ExecutionException("This engine can only execute [" + BatchTask.class.getName() + "]");
    }
    // Create persistence service for injection into analysis components
    TaskContext ctx = null;
    try {
        ctx = contextFactory.createContext(aConfiguration);
        // Now the setup is complete
        ctx.getLifeCycleManager().initialize(ctx, aConfiguration);
        // Start recording
        ctx.getLifeCycleManager().begin(ctx, aConfiguration);
        try {
            BatchTask cfg = (BatchTask) aConfiguration;
            ParameterSpace parameterSpace = cfg.getParameterSpace();
            // Try to calculate the parameter space size.
            int estimatedSize = 1;
            for (Dimension<?> d : parameterSpace.getDimensions()) {
                if (d instanceof FixedSizeDimension) {
                    FixedSizeDimension fsd = (FixedSizeDimension) d;
                    if (fsd.size() > 0) {
                        estimatedSize *= fsd.size();
                    }
                }
            }
            // A subtask execution may apply to multiple parameter space coordinates!
            Set<String> executedSubtasks = new LinkedHashSet<String>();
            ProgressMeter progress = new ProgressMeter(estimatedSize);
            for (Map<String, Object> config : parameterSpace) {
                if (cfg.getConfiguration() != null) {
                    for (Entry<String, Object> e : cfg.getConfiguration().entrySet()) {
                        if (!config.containsKey(e.getKey())) {
                            config.put(e.getKey(), e.getValue());
                        }
                    }
                }
                log.info("== Running new configuration [" + ctx.getId() + "] ==");
                List<String> keys = new ArrayList<String>(config.keySet());
                for (String key : keys) {
                    log.info("[" + key + "]: [" + StringUtils.abbreviateMiddle(Util.toString(config.get(key)), "…", 150) + "]");
                }
                executeConfiguration(cfg, ctx, config, executedSubtasks);
                progress.next();
                log.info("Completed configuration " + progress);
            }
            // Set the subtask property and persist again, so the property is available to
            // reports
            cfg.setAttribute(SUBTASKS_KEY, executedSubtasks.toString());
            cfg.persist(ctx);
        } catch (LifeCycleException e) {
            ctx.getLifeCycleManager().fail(ctx, aConfiguration, e);
            throw e;
        } catch (UnresolvedImportException e) {
            // HACK - pass unresolved import exceptions up to the outer batch task
            ctx.getLifeCycleManager().fail(ctx, aConfiguration, e);
            throw e;
        } catch (Throwable e) {
            ctx.getLifeCycleManager().fail(ctx, aConfiguration, e);
            throw new ExecutionException(e);
        }
        // End recording (here the reports will nbe done)
        ctx.getLifeCycleManager().complete(ctx, aConfiguration);
        return ctx.getId();
    } finally {
        if (ctx != null) {
            ctx.getLifeCycleManager().destroy(ctx, aConfiguration);
        }
    }
}

Example 67 with ParameterSpace

use of org.dkpro.lab.task.ParameterSpace in project dkpro-tc by dkpro.

the class ExperimentBuilder method build.

/**
 * Wires all provided information into a parameter space object that can be provided to an
 * experiment
 *
 * @return the parameter space filled with the provided information
 */
public ParameterSpace build() {
    List<Dimension<?>> dimensions = new ArrayList<>();
    dimensions.add(getAsDimensionMachineLearningAdapter());
    dimensions.add(getAsDimensionFeatureMode());
    dimensions.add(getAsDimensionLearningMode());
    dimensions.add(getAsDimensionFeatureSets());
    dimensions.add(getAsDimensionReaders());
    ParameterSpace ps = new ParameterSpace();
    ps.setDimensions(dimensions.toArray(new Dimension<?>[0]));
    return ps;
}

Example 68 with ParameterSpace

use of org.dkpro.lab.task.ParameterSpace in project dkpro-tc by dkpro.

the class WekaDocumentPlain method getParameterSpace.

public static ParameterSpace getParameterSpace() throws ResourceInitializationException {
    // configure training and test data reader dimension
    // train/test will use both, while cross-validation will only use the
    // train part
    Map<String, Object> dimReaders = new HashMap<String, Object>();
    CollectionReaderDescription readerTrain = CollectionReaderFactory.createReaderDescription(FolderwiseDataReader.class, FolderwiseDataReader.PARAM_SOURCE_LOCATION, corpusFilePathTrain, FolderwiseDataReader.PARAM_LANGUAGE, LANGUAGE_CODE, FolderwiseDataReader.PARAM_PATTERNS, "*/*.txt");
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    CollectionReaderDescription readerTest = CollectionReaderFactory.createReaderDescription(FolderwiseDataReader.class, FolderwiseDataReader.PARAM_SOURCE_LOCATION, corpusFilePathTest, FolderwiseDataReader.PARAM_LANGUAGE, LANGUAGE_CODE, FolderwiseDataReader.PARAM_PATTERNS, "*/*.txt");
    dimReaders.put(DIM_READER_TEST, readerTest);
    Dimension<TcFeatureSet> dimFeatureSets = Dimension.create(DIM_FEATURE_SET, new TcFeatureSet("DummyFeatureSet", TcFeatureFactory.create(TokenRatioPerDocument.class), TcFeatureFactory.create(WordNGram.class, WordNGram.PARAM_NGRAM_USE_TOP_K, 20, WordNGram.PARAM_NGRAM_MIN_N, 1, WordNGram.PARAM_NGRAM_MAX_N, 3)));
    Map<String, Object> config = new HashMap<>();
    config.put(DIM_CLASSIFICATION_ARGS, new Object[] { new WekaAdapter(), NaiveBayes.class.getName() });
    config.put(DIM_DATA_WRITER, new WekaAdapter().getDataWriterClass().getName());
    config.put(DIM_FEATURE_USE_SPARSE, new WekaAdapter().useSparseFeatures());
    Dimension<Map<String, Object>> mlas = Dimension.createBundle("config", config);
    ParameterSpace pSpace = new ParameterSpace(Dimension.createBundle("readers", dimReaders), Dimension.create(DIM_LEARNING_MODE, LM_SINGLE_LABEL), Dimension.create(DIM_FEATURE_MODE, FM_DOCUMENT), dimFeatureSets, mlas);
    return pSpace;
}

Example 69 with ParameterSpace

use of org.dkpro.lab.task.ParameterSpace in project dkpro-tc by dkpro.

the class WekaDocumentPlain method main.

public static void main(String[] args) throws Exception {
    DemoUtils.setDkproHome("target/");
    ParameterSpace pSpace = getParameterSpace();
    WekaDocumentPlain experiment = new WekaDocumentPlain();
    experiment.runTrainTest(pSpace);
}

Example 70 with ParameterSpace

use of org.dkpro.lab.task.ParameterSpace in project dkpro-tc by dkpro.

the class WekaManualFoldCrossValidation method getParameterSpace.

public static ParameterSpace getParameterSpace(boolean manualFolds) throws ResourceInitializationException {
    Map<String, Object> dimReaders = new HashMap<String, Object>();
    dimReaders.put(DIM_READER_TRAIN, BrownCorpusReader.class);
    CollectionReaderDescription readerTrain = CollectionReaderFactory.createReaderDescription(BrownCorpusReader.class, BrownCorpusReader.PARAM_LANGUAGE, "de", BrownCorpusReader.PARAM_SOURCE_LOCATION, corpusFilePathTrain, BrownCorpusReader.PARAM_PATTERNS, INCLUDE_PREFIX + "*.xml");
    dimReaders.put(DIM_READER_TRAIN, readerTrain);
    Dimension<TcFeatureSet> dimFeatureSets = Dimension.create(DIM_FEATURE_SET, new TcFeatureSet(TcFeatureFactory.create(CharacterNGram.class, CharacterNGram.PARAM_NGRAM_MIN_N, 2, CharacterNGram.PARAM_NGRAM_MAX_N, 3, CharacterNGram.PARAM_NGRAM_USE_TOP_K, 750)));
    Map<String, Object> config = new HashMap<>();
    config.put(DIM_CLASSIFICATION_ARGS, new Object[] { new WekaAdapter(), NaiveBayes.class.getName() });
    config.put(DIM_DATA_WRITER, new WekaAdapter().getDataWriterClass().getName());
    config.put(DIM_FEATURE_USE_SPARSE, new WekaAdapter().useSparseFeatures());
    Dimension<Map<String, Object>> mlas = Dimension.createBundle("config", config);
    ParameterSpace pSpace = new ParameterSpace(Dimension.createBundle("readers", dimReaders), Dimension.create(DIM_LEARNING_MODE, LM_SINGLE_LABEL), Dimension.create(DIM_FEATURE_MODE, FM_UNIT), dimFeatureSets, mlas, /*
                 * MANUAL CROSS VALIDATION FOLDS - i.e. the cas created by your reader will be used
                 * as is to make folds
                 */
    Dimension.create(DIM_CROSS_VALIDATION_MANUAL_FOLDS, manualFolds));
    return pSpace;
}